[Suspected pulmonary nodule in a male heavy smoker: Is biopsy really necessary?] / Nodule pulmonaire suspect chez un grand tabagique : faut-il faire une biopsie ?

INTRODUCTION: Sclerotherapy is a widely used as a lifesaving therapeutic option in cases of upper gastrointestinal bleeding (UGB) due to ruptured gastro-esophageal varices (GOV) in cirrhotic patients, especially when there exists a portosystemic shunt. This endoscopic technique can entail many complications, including systemic and non-thrombotic pulmonary embolism (PE). While multiple pulmonary parenchymal manifestations have been described following sclerotherapy of GOV, to our knowledge no solitary suspicious pulmonary nodule has been described. CASE PRESENTATION: We report the case of 55-year-old man with heavy smoking history who was referred to our pulmonary clinic for work-up of a solitary pulmonary nodule. He was known to have liver cirrhosis with history of massive UGB due to rupture of GOV two months before. He was treated with sclerotherapy by injecting a 3 cc of Histoacryl/lipiodole solution. The post- endoscopic phase was unremarkable. An enhanced CT scan of chest and abdomen performed two months later showed a right upper lobe nodule, even though at that point, the patient was completely asymptomatic. This was ascribed to non-thrombotic PE secondary to sclerotherapy due to complete resolution of the nodule on a CT scan carried out at 2-month follow-up. At that point, his condition did not require any further treatment. CONCLUSION: Solitary pulmonary nodule is one of the radiologic manifestations of PE subsequent to sclerotherapy of GOV. Awareness and radiologic follow-up of this unusual radiologic presentation may prevent unnecessary biopsies.

Developing a multi-institutional nomogram for assessing lung cancer risk in patients with 5-30 mm pulmonary nodules: a retrospective analysis.

Background: The diagnosis of benign and malignant solitary pulmonary nodules based on personal experience has several limitations. Therefore, this study aims to establish a nomogram for the diagnosis of benign and malignant solitary pulmonary nodules using clinical information and computed tomography (CT) results. Methods: Retrospectively, we collected clinical and CT characteristics of 1,160 patients with pulmonary nodules in Guang'an People's Hospital and the hospital affiliated with North Sichuan Medical College between 2019 and 2021. Among these patients, data from 773 patients with pulmonary nodules were used as the training set. We used the least absolute shrinkage and selection operator (LASSO) to optimize clinical and imaging features and performed a multivariate logistic regression to identify features with independent predictive ability to develop the nomogram model. The area under the receiver operating characteristic curve (AUC), C-index, decision curve analysis, and calibration plot were used to evaluate the performance of the nomogram model in terms of predictive ability, discrimination, calibration, and clinical utility. Finally, data from 387 patients with pulmonary nodules were utilized for validation. Results: In the training set, the predictors for the nomogram were gender, density of the nodule, nodule diameter, lobulation, calcification, vacuole, vascular convergence, bronchiole, and pleural traction, selected through LASSO and logistic regression analysis. The resulting model had a C-index of 0.842 (95% CI [0.812-0.872]) and AUCs of 0.842 (95% CI [0.812-0.872]). In the validation set, the C-index was 0.856 (95% CI [0.811-0.901]), and the AUCs were 0.844 (95% CI [0.797-0.891]). Results from the calibration curve and clinical decision curve analyses indicate that the nomogram has a high fit and clinical benefit in both the training and validation sets. Conclusion: The establishment of a nomogram for predicting the benign or malignant diagnosis of solitary pulmonary nodules by this study has shown good efficacy. Such a nomogram may help to guide the diagnosis, follow-up, and treatment of patients.

Novel electromagnetic navigation bronchoscopy system for the diagnosis of peripheral pulmonary nodules: a prospective, multicentre study.

BACKGROUND: Traditional electromagnetic navigation bronchoscopy (ENB) is a real-time image-guided system and used with thick bronchoscopes for the diagnosis of peripheral pulmonary nodules (PPNs). A novel ENB that could be used with thin bronchoscopes was developed. This study aimed to evaluate the diagnostic yield and the experience of using this ENB system in a real clinical scenario. METHODS: This multicentre study enrolled consecutive patients with PPNs adopting ENB from March 2019 to August 2021. ENB was performed with different bronchoscopes, ancillary techniques and sampling instruments according to the characteristics of the nodule and the judgement of the operator. The primary endpoint was the diagnostic yield. The secondary endpoints included the diagnostic yield of subgroups, procedural details and complication rate. RESULTS: In total, 479 patients with 479 nodules were enrolled in this study. The median lesion size was 20.9 (IQR, 15.9-25.9) mm. The overall diagnostic yield was 74.9% (359/479). A thin bronchoscope was used in 96.2% (461/479) nodules. ENB in combination with radial endobronchial ultrasound (rEBUS), a guide sheath (GS) and a thin bronchoscope was the most widely used guided method, producing a diagnostic yield of 74.1% (254/343). The median total procedural time was 1325.0 (IQR, 1014.0-1676.0) s. No severe complications occurred. CONCLUSION: This novel ENB system can be used in combination with different bronchoscopes, ancillary techniques and sampling instruments with a high diagnostic yield and safety profile for the diagnosis of PPNs, of which the combination of thin bronchoscope, rEBUS and GS was the most common method in clinical practice. TRIAL REGISTRATION NUMBER: NCT03716284.

Serum lncRNA THRIL predicts benign and malignant pulmonary nodules and promotes the progression of pulmonary malignancies.

BACKGROUND: This project aimed to research the significance of THRIL in the diagnosis of benign and malignant solitary pulmonary nodules (SPNs) and to investigate the role of THRIL/miR-99a in malignant SPNs. METHODS: The study groups consisted of 169 patients with SPN and 74 healthy subjects. The differences in THRIL levels were compared between the two groups and the healthy group. The receiver operating characteristic curve (ROC) was utilized to analyze the THRIL's significance in detecting benign and malignant SPN. Pearson correlation and binary regression coefficients represented the association between THRIL and SPN. CCK-8 assay, Transwell assay, and flow cytometry were utilized to detect the regulatory effect of THRIL silencing. The interaction between THRIL, miR-99a, and IGF1R was confirmed by the double luciferase reporter gene. RESULTS: There were differences in THRIL expression in the healthy group, benign SPN group, and malignant SPN group. High accuracy of THRIL in the diagnosis of benign SPN and malignant SPN was observed. THRIL was associated with the development of SPN. The expression of THRIL was upregulated and miR-99a was downregulated in lung cancer cells. The double luciferase report experiment confirmed the connections between THRIL/miR-99a/IGF1R. Silencing THRIL could suppress cell proliferation, migration, and invasion and promote cell apoptosis by binding miR-99a. CONCLUSION: The detection of THRIL in serum is useful for the assessment of malignant SPN. THRIL can regulate the expression of IGF1R through miR-99a, thereby promoting the growth of lung cancer cells and inhibiting apoptosis.

Single Setting Robotic Lung Nodule Diagnosis and Resection.

Lung cancer remains the leading cause of cancer-related deaths. Early tissue diagnosis followed by timely therapeutic procedures can have a significant impact on overall survival. While robotic-assisted lung resection is an established therapeutic procedure, robotic-assisted bronchoscopy is a more recent diagnostic procedure that improves reach, stability, and precision in the field of bronchoscopic lung nodule biopsy. The ability to combine lung cancer diagnostics with therapeutic surgical resection into a single-setting anesthesia procedure has the potential to decrease costs, improve patient experiences, and most importantly, reduce delays in cancer care.

A combined diagnostic model based on circulating tumor cell in patients with solitary pulmonary nodules.

BACKGROUND: Although many prediction models in diagnosis of solitary pulmonary nodules (SPNs) have been developed, few are widely used in clinical practice. It is therefore imperative to identify novel biomarkers and prediction models supporting early diagnosis of SPNs. This study combined folate receptor-positive circulating tumor cells (FR+ CTC) with serum tumor biomarkers, patient demographics and clinical characteristics to develop a prediction model. METHODS: A total of 898 patients with a solitary pulmonary nodule who received FR+ CTC detection were randomly assigned to a training set and a validation set in a 2:1 ratio. Multivariate logistic regression was used to establish a diagnostic model to differentiate malignant and benign nodules. The receiver operating curve (ROC) and the area under the curve (AUC) were calculated to assess the diagnostic efficiency of the model. RESULTS: The positive rate of FR+ CTC between patients with non-small cell lung cancer (NSCLC) and benign lung disease was significantly different in both the training and the validation dataset (p < 0.001). The FR+ CTC level was significantly higher in the NSCLC group compared with that of the benign group (p < 0.001). FR+ CTC (odds ratio, OR, 95% confidence interval, CI: 1.13, 1.07-1.19, p < 0.0001), age (OR, 95% CI: 1.06, 1.01-1.12, p = 0.03) and sex (OR, 95% CI: 1.07, 1.01-1.13, p = 0.01) were independent risk factors of NSCLC in patients with a solitary pulmonary nodule. The area under the curve (AUC) of FR+ CTC in diagnosing NSCLC was 0.650 (95% CI, 0.587-0.713) in the training set and 0.700 (95% CI, 0.603-0.796) in the validation set, respectively. The AUC of the combined model was 0.725 (95% CI, 0.659-0.791) in the training set and 0.828 (95% CI, 0.754-0.902) in the validation set, respectively. CONCLUSIONS: We confirmed the value of FR+ CTC in diagnosing SPNs and developed a prediction model based on FR+ CTC, demographic characteristics, and serum biomarkers for differential diagnosis of solitary pulmonary nodules.

Incidence of Clinically Relevant Solitary Pulmonary Nodules Utilizing a Universal Health Care System.

INTRODUCTION: Solitary pulmonary nodules (SPNs) are common, but the clinical relevance of these nodules is unknown. Utilizing current screening guidelines, we sought to better characterize the national incidence of clinically important SPNs within the largest universal health care system in the nation. MATERIALS AND METHODS: TRICARE data were queried to identify SPNs for ages 18-64 years. SPNs that had been diagnosed within a year with no prior oncologic history were included to ensure true incidence. A proprietary algorithm was applied to determine clinically significant nodules. Further analysis characterized incidence by age grouping, gender, region, military branch, and beneficiary status. RESULTS: A total of 229,552 SPNs were identified with a 60% reduction seen after application of the clinical significance algorithm (N = 88,628). The incidence increased in each decade of life (all P < 0.01). Adjusted incident rate ratios were significantly higher for SPNs detected in the Midwest and Western regions. The incident rate ratio was also higher in females (1.05, confidence interval [CI] 1.018, P = 0.001) as well as non-active duty members (dependents = 1.4 and retired = 1.6, respectively, CIs 1.383-1.492 and 1.591-1.638, P < 0.01). The incidence calculated per 1,000 patients overall was 3.1/1,000. Ages 44-54 years had an incidence of 5.5/1,000 patients, which is higher than the previously reported incidence of < 5.0 nationally for the same age group. CONCLUSIONS: This analysis represents the largest evaluation of SPNs to date combined with clinical relevance adjustment. These data suggest a higher incidence of clinically significant SPNs starting at an age of 44 years in nonmilitary or retired women localized to the Midwest and Western regions of the United States.

All-in-one diagnostic and therapeutic precision thoracic surgery using a hybrid operating theatre: the triple-marking technique.

BACKGROUND: The minimally invasive management of sub-centimetric and often sub-solid lung lesions is quite challenging for thoracic surgeons. As a matter of fact, thoracoscopic wedge resection can often require conversion to thoracotomy when pulmonary lesions cannot be visually identified. Hybrid operating rooms (ORs) can serve as a helpful tool in a multidisciplinary setting, providing real-time lesion imaging and targeting, allowing preoperative or intraoperative percutaneous placement of different lesions targeting techniques to help locate non-palpable lung nodules during video-assisted thoracic surgery. The aim of the study is to assess whether the lung nodule marking using methylene blue, indocyanine green, and gold seeds - the "triple-marking technique" - in the hybrid OR is effective in helping locate non-visible or palpable nodules. METHODS: We conducted a retrospective study on 19 patients with non-palpable lung lesions requiring VATS wedge resection and underwent lesional targeting in the hybrid operating room with different marking systems, including gold seeds placement, methylene blue, or indocyanine green. Lesions were considered non-palpable due to sizing, radiological subsolid aspect, or location and then identified using intraoperative CT scans, also allowing to elaborate needle trajectory. The intraoperative diagnosis was obtained in all of the patients guiding the type of surgery performed. RESULTS: The radio-opaque gold seed marker was used in all of the patients except for two cases that developed intraprocedural pneumothoraces with no major consequences. In these patients, the nodule marking using dyes was still performed and successful in allowing to locate the lesion. Methylene blue and indocyanine green were always used in combination during the dye-targeting phase. Methylene blue appeared to be non-visible in two patients. The indocyanine green was correctly visualized in every patient. We observed the gold seed dislocation in two patients. We were able to identify the lung lesion in all the patients correctly. No conversion was needed. No allergic reactions were observed due to dye administration, and no prophylaxis was performed prior to lesional marking. The lung lesions were visually identified in 100% of the patients thanks to at least one marking technique. CONCLUSIONS: Our experience confirms that the hybrid operating room can represent a suitable tool in helping locate hard-to-find lung lesions in planned VATS resections. Using different techniques, a multiple marking approach seems advisable to maximize the lung lesions detecting rate by direct vision, therefore reducing the VATS conversion rate.

Clinical Characteristics of Human Pulmonary Dirofilariasis in Japan: An Uncommon Differential Diagnosis of a Solitary Pulmonary Nodule.

Human pulmonary dirofilariasis (HPD) is a zoonotic disease caused by Dirofilaria immitis. Most HPD cases are asymptomatic and are either detected during annual health checkups or incidentally identified during the investigation of other diseases, particularly primary or metastatic pulmonary lung cancers. However, the frequency and clinical features of Japanese patients with HPD remain unclear. We analyzed data from the Japanese Medical Abstract Society database and identified 69 cases between 1978 and 2022. The incidence of HPD increased until the 2000s but declined markedly in the 2010s. The incidence is higher in the southwestern region and lower in the northeastern region of Japan. Health checkups are the primary diagnostic opportunities. The Chugoku and Shikoku regions have had high incidence rates per population. The diagnosis of HPD using a noninvasive procedure is typically difficult because of the absence of specific clinical symptoms, and approximately 70% of the cases are detected using video-assisted thoracoscopic surgery. Climate change may increase the incidence of HPD in the northeastern region of Japan, and travel to countries with poor vector control may be a risk factor for HPD transmission. Physicians should consider this parasitic infectious disease when examining patients presenting with solitary lung nodules.

Differences in VA and Non-VA Pulmonary Nodules: All Evaluations Are not Created Equal.

BACKGROUND: Indeterminate pulmonary nodules present a common challenge for clinicians who must recommend surveillance or intervention based on an assessed risk of malignancy. PATIENTS AND METHODS: In this cohort study, patients presenting for indeterminate pulmonary nodule evaluation were enrolled at sites participating in the Colorado SPORE in Lung Cancer. They were followed prospectively and included for analysis if they had a definitive malignant diagnosis, benign diagnosis, or radiographic resolution or stability of their nodule for > 2 years. RESULTS: Patients evaluated at the Veterans Affairs (VA) and non-VA sites were equally as likely to have a malignant diagnosis (48%). The VA cohort represented a higher-risk group than the non-VA cohort regarding smoking history and chronic obstructive pulmonary disease (COPD). There were more squamous cell carcinoma diagnoses among VA malignant nodules (25% vs. 10%) and a later stage at diagnosis among VA patients. Discrimination and calibration of risk calculators produced estimates that were wide-ranging and different when comparing between risk score calculators as well as between VA/non-VA cohorts. Application of current American College of Chest Physicians guidelines to our groups could have resulted in inappropriate resection of 12% of benign nodules. CONCLUSION: Comparison of VA with non-VA patients shows important differences in underlying risk, histology of malignant nodules, and stage at diagnosis. This study highlights the challenge in applying risk calculators to a clinical setting, as the model discrimination and calibration were variable between calculators and between our higher-risk VA and lower-risk non-VA groups. MICROABSTRACT: Risk stratification and management of indeterminate pulmonary nodules (IPNs) is a common clinical problem. In this prospective cohort study of 282 patients with IPNs from Veterans Affairs (VA) and non-VA sites, we found differences in patient and nodule characteristics, histology and diagnostic stage, and risk calculator performance. Our findings highlight challenges and shortcomings of current IPN management guidelines and tools.

Pulmonary Tuberculosis with Elevated CEA and Positive PET-CT.

BACKGROUND: Pulmonary tuberculosis presenting as solitary pulmonary nodules in imaging is sometimes difficult to differentiate from lung cancer and is more likely to be misdiagnosed when accompanied by elevated CEA and positive PET-CT findings. METHODS: By reporting a case of misdiagnosed lung cancer, which was confirmed to be pulmonary tuberculosis by lung biopsy, a joint literature analysis was performed to raise clinicians' awareness of isolated nodules in the lung. RESULTS: With a series of ancillary tests, we initially considered the nodule to be malignant, and the lung biopsy pathology eventually confirmed pulmonary tuberculosis. CONCLUSIONS: When chest imaging suggests the presence of malignant features in solitary pulmonary nodules, invasive procedures can be performed appropriately to clarify the nature of the lesion. The diagnosis cannot be made blindly to ensure that no incorrect diagnosis is made nor wrong treatment given.

[Echinococcosis Detected by a Solitary Shadow in the Lung].

Here we report a rare case of pulmonary coin lesion due to echinococcosis. An woman in her 60s who has no symptom was found a nodular shadow of the left lung incidentally. Since the nodule was enlarging, surgical treatment was done. Pathologically, it was diagnosed as an echinococcosis of the lung. It was pulmonary solitary echinococcosis without any lesion in other organs.

Prevalence and consequences of non-adherence to an evidence-based approach for incidental pulmonary nodules.

IMPORTANCE: Distinguishing benign from malignant pulmonary nodules is challenging. Evidence-based guidelines exist, but their impact on patient-centered outcomes is unknown. OBJECTIVE: To understand if the evaluation of incidental pulmonary nodules that follows an evidence-based management strategy is associated with fewer invasive procedures for benign lesions and/or fewer delays in cancer diagnosis. DESIGN: Retrospective cohort study. SETTING: Large academic medical center. PARTICIPANTS: Adults (≥18 years age) with an incidental pulmonary nodule discovered between January 2012 and December 2014. Patients with calcified nodules, prior nodules, prior diagnosis of cancer, high suspicion for pulmonary metastasis, or limited life expectancy were excluded. EXPOSURE: Nodule management strategy (pre-specified based on evidence-based practices). OUTCOME: Composite of any invasive procedure for a benign nodule or delay in diagnosis in patients with cancer (>3 month delay once probability of cancer was >15%). RESULTS: Of 314 patients that met inclusion criteria, median age was 61, 46.5% were men, and 66.5% had current or former tobacco use. The mean nodule size was 10.3 mm, mean probability of cancer was 11.8%, and 14.3% of nodules were malignant. Evaluation followed an evidence-based strategy in 245 patients (78.0%), and deviated in 69 patients (22%). The composite outcome occurred in 26 (8.3%) patients. Among patients whose nodule evaluation was concordant with an evidence-based evaluation, 6.1% (15/245) experienced the composite outcome versus 15.9% (11/69) of patients with an evaluation that deviated from evidence-based recommendations (P<0.01). CONCLUSIONS AND RELEVANCE: At a large academic medical center, more than 1 in 5 patients with an incidental pulmonary nodule underwent evaluation that deviated from evidence-based practice recommendations. Nodule evaluation that deviated from an evidence-based strategy was associated with biopsy of benign lesions and delays in cancer diagnosis, suggesting a need to improve guideline uptake.

Spiral CT Image Characteristics and Differential Diagnosis Secondary Pulmonary Tuberculosis and Lung Cancer Based on Visual Sensors.

Helical CT plain scan has high spatial and area resolution, which is beneficial to the extraction of CT features of pulmonary nodules, and is of great significance for the diagnosis and differential diagnosis of pulmonary diseases. In order to deeply study the role of visual sensor image algorithm in CT image, this paper adopts clinical simulation method, data fusion method, and image acquisition method to collect images, analyze CT image features, and simplify the algorithm and create a CT model that can better diagnose secondary tuberculosis and lung cancer. We selected 45 patients with lung disease in this group, with an average age of 38 years. At the same time, the consistency analysis results of the diameter and plain CT value data of the five groups of cases measured by two observers are between 0.82 and 0.88, which has a good consistency. We could find that the nodule diameters of the five groups of cases were different (F =16.99, P < 0.01), and the difference was statistically significant (P < 0.06), indicating that our data are not only accurate but also very reliable. ROC was used to analyze the precise value of CT values in the pulmonary tuberculosis group and lung cancer group, intrapulmonary lymph node group, and pulmonary hamartoma group to determine the cutoff value. The results showed that the AUC values of the pulmonary tuberculosis group and the lung cancer group were 0.788, and the middle was the largest, indicating that the values were guaranteed. The basic realization starts with visual sensor technology and designs a clinical model that can more accurately identify CT images and differential diagnosis.

Establishment and validation of a clinical model for diagnosing solitary pulmonary nodules.

OBJECTIVES: The main purpose of this study was to develop and validate a clinical model for estimating the risk of malignancy in solitary pulmonary nodules (SPNs). METHODS: A total of 672 patients with SPNs were retrospectively reviewed. The least absolute shrinkage and selection operator algorithm was applied for variable selection. A regression model was then constructed with the identified predictors. The discrimination, calibration, and clinical validity of the model were evaluated by the area under the receiver-operating-characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: Ten predictors, including gender, age, nodule type, diameter, lobulation sign, calcification, vascular convergence sign, mediastinal lymphadenectasis, the natural logarithm of carcinoembryonic antigen, and combination of cytokeratin 19 fragment 21-1, were incorporated into the model. The prediction model demonstrated valuable prediction performance with an AUC of 0.836 (95% CI: 0.777-0.896), outperforming the Mayo (0.747, p = 0.024) and PKUPH (0.749, p = 0.018) models. The model was well-calibrated according to the calibration curves. The DCA indicated the nomogram was clinically useful over a wide range of threshold probabilities. CONCLUSION: This study proposed a clinical model for estimating the risk of malignancy in SPNs, which may assist clinicians in identifying the pulmonary nodules that require invasive procedures and avoid the occurrence of overtreatment.

[The multi-dimensional molecular characteristics of the indolent pulmonary ground-glass nodules].

With the dramatically increasing detection rate of ground-glass nodules (GGN), exact understanding and treatment strategy of them has become the hottest issue currently. More and more studies have begun to explore the underlying mechanisms of their indolent characteristics and favorable prognosis from the perspectives of molecular evolution and immune microenvironment. GGN has different dominating gene mutations at different evolutional stages. The pure GGN has a lower tumor mutation burden and genomic instability, while a gradually evolutionary feature of genomic mutation along with the pathological progression can be observed. GGN has less infiltration of immune cells, and they are under the pressure of immune surveillance with weakened immune escape. With the increase of solid components, an inhibitory immune microenvironment is gradually established and immune escape is gradually enhanced, leading to rapid tumor growth. Further exploration of the molecular characteristics of GGN will help to more precisely distinguish these highly heterogeneous lesions, which could be helpful to make personalized treatment plans.

[The diagnostic value of machine-learning-based model for predicting the malignancy of solid nodules in multiple pulmonary nodules].

Objective: To examine the efficiacy of a machine learning diagnostic model specifically for solid nodules in multiple pulmonary nodules constructed by combining patient clinical information and CT features. Methods: Totally 446 solid nodules resected from 287 patients with multiple pulmonary nodules in Department of Thoracic Surgery, Peking University People's Hospital from January 2010 to December 2018 were included. There were 117 males and 170 females, aging (61.4±9.9) yeras (range: 33 to 84 years). The nodules were randomly divided into training set (228 patients with 357 nodules) and test set (59 patients with 89 nodules) by a ratio of 4∶1. The extreme gradient boosting (XGBoost) algorithm was used to generate a predictive model (PKU-ML model) on the training set. The accuracy was verified on the test set and compared with previous published models. Finally, an independent single solid nodule set (155 patients, 95 males, aging (62.3±8.3) years (range: 37 to 77 years)) was used to evaluate the accuracy of the model for predictive value of single solid nodules. Area of receiver operating characteristic curve (AUC) was used to evaluate diagnostic values of models. Results: In the training set, the AUC of the PKU-ML model was 0.883 (95%CI: 0.849 to 0.917). In the test set, the performance of the PKU-ML model (AUC=0.838, 95%CI: 0.754 to 0.921) was better than the models designed for single pulmonary nodules (Brock model: AUC=0.709, 95%CI: 0.603 to 0.816, P=0.04; Mayo model: AUC=0.756, 95%CI: 0.656 to 0.856, P=0.01; VA model: AUC=0.674, 95%CI: 0.561 to 0.787, P<0.01), similar with PKUPH model (AUC=0.750, 95%CI: 0.649 to 0.851, P=0.07). In the independent single solid nodules set, the PKU-ML model also achieved good performance (AUC=0.786, 95%CI: 0.701 to 0.872). Conclusion: The machine learning based PKU-ML model can better predict the malignancy of solid nodules in multiple pulmonary nodules, and also achieved a good performance in predicting the malignancy of single solid pulmonary nodules compared to mathematical models.

Role of biomarkers in lung nodule evaluation.

PURPOSE OF REVIEW: Worldwide, lung cancer is the leading cause of cancer mortality. Much of this mortality is thought to be secondary to detection in later stages, where treatment options and survivability are limited. The goals of lung nodule evaluation are to expedite the diagnosis and treatment of patients with malignant nodules and to minimize unnecessary diagnostic procedures in those with benign nodules. However, the differentiation between benign and malignant has been challenging and is further complicated by the benefits of early diagnosis competing with potential morbidity of invasive diagnostic procedures. RECENT FINDINGS: Biomarkers have the potential to improve estimates of pretest probability of malignancy in pulmonary nodules, especially in the intermediate-risk subgroup. Four biomarkers have undergone extensive validation and are available for clinical use, and we will discuss each in this review. SUMMARY: The application of biomarkers to lung cancer risk assessment has the potential to improve cancer probability assessments, which in turn can reduce unnecessary invasive testing and/or reduce delays in diagnosis and treatment.

Hybrid U-Net-based deep learning model for volume segmentation of lung nodules in CT images.

OBJECTIVE: Accurate segmentation of the lung nodule in computed tomography images is a critical component of a computer-assisted lung cancer detection/diagnosis system. However, lung nodule segmentation is a challenging task due to the heterogeneity of nodules. This study is to develop a hybrid deep learning (H-DL) model for the segmentation of lung nodules with a wide variety of sizes, shapes, margins, and opacities. MATERIALS AND METHODS: A dataset collected from Lung Image Database Consortium image collection containing 847 cases with lung nodules manually annotated by at least two radiologists with nodule diameters greater than 7 mm and less than 45 mm was randomly split into 683 training/validation and 164 independent test cases. The 50% consensus consolidation of radiologists' annotation was used as the reference standard for each nodule. We designed a new H-DL model combining two deep convolutional neural networks (DCNNs) with different structures as encoders to increase the learning capabilities for the segmentation of complex lung nodules. Leveraging the basic symmetric U-shaped architecture of U-Net, we redesigned two new U-shaped deep learning (U-DL) models that were expanded to six levels of convolutional layers. One U-DL model used a shallow DCNN structure containing 16 convolutional layers adapted from the VGG-19 as the encoder, and the other used a deep DCNN structure containing 200 layers adapted from DenseNet-201 as the encoder, while the same decoder with only one convolutional layer at each level was used in both U-DL models, and we referred to them as the shallow and deep U-DL models. Finally, an ensemble layer was used to combine the two U-DL models into the H-DL model. We compared the effectiveness of the H-DL, the shallow U-DL and the deep U-DL models by deploying them separately to the test set. The accuracy of volume segmentation for each nodule was evaluated by the 3D Dice coefficient and Jaccard index (JI) relative to the reference standard. For comparison, we calculated the median and minimum of the 3D Dice and JI over the individual radiologists who segmented each nodule, referred to as M-Dice, min-Dice, M-JI, and min-JI. RESULTS: For the 164 test cases with 327 nodules, our H-DL model achieved an average 3D Dice coefficient of 0.750 ± 0.135 and an average JI of 0.617 ± 0.159. The radiologists' average M-Dice was 0.778 ± 0.102, and the average M-JI was 0.651 ± 0.127; both were significantly higher than those achieved by the H-DL model (p < 0.05). The radiologists' average min-Dice (0.685 ± 0.139) and the average min-JI (0.537 ± 0.153) were significantly lower than those achieved by the H-DL model (p < 0.05). The results indicated that the H-DL model approached the average performance of radiologists and was superior to the radiologist whose manual segmentation had the min-Dice and min-JI. Moreover, the average Dice and average JI achieved by the H-DL model were significantly higher than those achieved by the individual shallow U-DL model (Dice of 0.745 ± 0.139, JI of 0.611 ± 0.161; p < 0.05) or the individual deep U-DL model alone (Dice of 0.739 ± 0.145, JI of 0.604 ± 0.163; p < 0.05). CONCLUSION: Our newly developed H-DL model outperformed the individual shallow or deep U-DL models. The H-DL method combining multilevel features learned by both the shallow and deep DCNNs could achieve segmentation accuracy comparable to radiologists' segmentation for nodules with wide ranges of image characteristics.